61 citations · 321 across the 27 of their papers we have counts for
6 papers · 1 filter
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale
Eli Sherman, Hitinder Gurm, Ulysses Balis +2
In healthcare, patient risk stratification models are often learned using time-series data extracted from electronic health records. When extracting data for a clinical prediction…
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks
Jeeheh Oh, Jiaxuan Wang, Jenna Wiens
Recently, researchers have started applying convolutional neural networks (CNNs) with one-dimensional convolutions to clinical tasks involving time-series data. This is due, in par…
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images
Pascal Sturmfels, Saige Rutherford, Mike Angstadt +3
Given the wide success of convolutional neural networks (CNNs) applied to natural images, researchers have begun to apply them to neuroimaging data. To date, however, exploration o…
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
Ian Fox, Lynn Ang, Mamta Jaiswal +2
In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This…
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
Jiaxuan Wang, Ian Fox, Jonathan Skaza +3
During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a difficult decision when defending against the Cavaliers: "Do you double and risk giving up easy shots, or…
Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment
Dev Goyal, Zeeshan Syed, Jenna Wiens
Longitudinal patient data has the potential to improve clinical risk stratification models for disease. However, chronic diseases that progress slowly over time are often heterogen…